|Binary hit-miss operation|
|Morphological operations on binary images|
|Morphological operations on binary volume|
|Reduce all objects to lines in 2-D binary image or 3-D binary volume|
|Remove small objects from binary image|
|Suppress light structures connected to image border|
|Morphologically close image|
|Fill image regions and holes|
|Morphologically open image|
|Create connectivity array|
|Check validity of connectivity argument|
Dilation adds pixels to boundary of an object. Dilation makes objects more visible and fills in small holes in the object.
Erosion removes pixels from the boundary of an object. Erosion removes islands and small objects so that only substantive objects remain.
Combine dilation and erosion to remove small objects from an image and smooth the border of large objects.
Dilation adds pixels to the boundary of objects in an image. Erosion removes pixels from object boundaries.
A structuring element defines the neighborhood used to process each pixel. It influences the size and shape of objects you want to process in the image.
Morphological reconstruction is useful to extract marked objects from an image without changing the object size or shape.
The process of skeletonization reduces all objects in an image to lines, without changing the essential structure of the image.
The perimeter, or boundary, of objects in a binary image consists of all pixels at the interface of the object and the background.
This example shows how to enhance an image as a preprocessing step before analysis.
A lookup table is a vector in which each element represents the different permutations of pixels in a neighborhood. Lookup tables are useful for custom erosion and dilation operations.